Modern medical facilities generate huge amounts of patient related data (hereinafter “patient data”) and the amount of data generated by such facilities is likely to increase exponentially in the foreseeable future given new procedures, medical equipment (e.g., imaging, test, and therapy systems) diagnostic protocols, increasing specialization, and an increased ability to store large amounts of data. As in other industries, the medical industry has embraced electronic records, referred to in the industry as electronic medical records (EMRs), to store the onslaught of patient data for subsequent access.
Various software tools can be used to facilitate the speed and ease of data entry into an electronic medical record (EMR) application. For example, the Dragon Medical software application developed by Nuance Communications, Inc. implements speech recognition (SR) to transcribe spoken utterances into free-text fields in an EMR application. The Dragon Medical software application can also be programmed with voice commands for special keyboard inputs, such as the tab key, to allow the clinician to navigate between EMR fields using the voice commands.
The Clinical Language Understanding software application also developed by Nuance Communications, Inc., implements natural language processing (NLP) and a medical ontology (MO) to recognize where the content of a free-text field indicates a value for inclusion in a discrete EMR field and to generate a tag so that the value can be automatically entered and stored in the discrete field. The automatic input to discrete EMR fields saves time and reduces the expense required to ensure that clinicians properly enter data into discrete fields, for example, to facilitate reporting. Additionally, some clinicians find it more natural to use free text to create notes and to allow the computer to extract the appropriate discrete data. As used herein, the term “clinician” refers to any employee or agent of a medical facility including but not limited to doctors, nurses, clinicians, technicians, other clinicians, etc.
SR is typically implemented using acoustic models (AM), which provide probabilities that various utterances are signified by various sounds, and language models (LM), which provide probabilities that various utterances will occur based on the relationships between words. For example, to recognize a given sound as a given clinician uttering “hat”, having already decided that the clinician previously uttered, “the cat in the . . . ”, the SR multiplies the probabilities for various utterances as provided by the two models, and selects “hat” if it has the highest combined probability for occurring next. Because humans are more likely to follow rules of grammar as defined in an LM when dictating than when carrying on a conversation, a language model is more helpful when transcribing dictation than when transcribing a conversation.
Much of the data to be entered into an EMR application is also shared in conversation with a patient or a colleague. For example, information entered for an outpatient visit is typically gathered through an interview and examination of a patient, where most of the results of the examination are discussed verbally with the patient. Repeating the same information a second time using a software application such as Dragon Medical is redundant in the sense that the clinician is communicating the same information twice albeit to different audiences. As another example, much of the information certain clinicians enter into an EMR at a hospital is also discussed with other clinicians during rounds. Eliminating these redundancies could save significant time for clinicians.
The current practice of communicating the same information twice creates a dilemma for clinicians: either the clinician enters each piece of data into the EMR, or makes a note reminding themselves to do so immediately before or after communicating it to the patient or the clinician waits until the human-to-human conversation is finished before focusing on the data entry into the EMR. In the first case, the clinician is less attentive to the patient or other clinician. In the second case, there is an increased risk that the two communications may not contain the same information because it is sometimes difficult to remember all of the details of the conversation afterwards. Much like using a keyboard or a mouse, communication using a software application such as Dragon Medical requires the clinician's attention, in a manner such that the EMR continues to exhibit a “self-centered” personality.
In some cases, clinicians have employed a third person (often called a “medical scribe”) to passively listen to a conversation, for example, between the clinician and the patient. Listening to the conversation between the clinician and the patient, the third person recognizes at least some of the information discussed as applicable to an EMR field and enters the information into the EMR field immediately, at least as a rough draft. Over time, the third person may notice similarities between large sections of the many conversations a clinician has with patients and colleagues over the course of a day because, though the conversations differ in detail, a great deal of the flow of the conversation is predictable based on the clinician practice area or common ailments of patients. As the third person learns to recognize the predictable patterns of the conversations, it becomes easier for them to discern what is being said despite foreign accents and to respond appropriately using the EMR application. As a specific example, the third person may recognize patterns in the occurrence of various topics which arise during the conversation such as “measure blood-pressure”, “determine patient concerns”, “justify diagnosis”, etc.
Additionally, an EMR system may include media that would enhance a conversation between a clinician and a patient or other clinicians, but the clinician is not likely to utilize such media because the EMR system generally requires that the clinician search for the related media. For example, the media may include a map to facilitate a conversation concerning an appointment at another facility or the pickup of a medication; a picture and a biography of a member of a care team to facilitate a discussion about them; a picture of a medication and/or of equipment to be purchased; a graph of a patient's history and/or prognosis; a video of a prescribed exercise; a picture of good and/or bad outcomes; a picture of body parts being discussed, etc. Such media is typically related to a “topic” of the conversation.